Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Digital Intuition: Applying Common Sense Using Dimensionality Reduction
IEEE Intelligent Systems
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Discourse level opinion interpretation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Common sense computing: from the society of mind to digital intuition and beyond
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Sentic computing: exploitation of common sense for the development of emotion-sensitive systems
COST'09 Proceedings of the Second international conference on Development of Multimodal Interfaces: active Listening and Synchrony
Sentic medoids: organizing affective common sense knowledge in a multi-dimensional vector space
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Switching between different ways to think: multiple approaches to affective common sense reasoning
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
Sentic Computing for social media marketing
Multimedia Tools and Applications
Humor as circuits in semantic networks
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Image and Vision Computing
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In a world in which millions of people express their feelings and opinions about any issue in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information is a challenging task. In this work we build a knowledge base which merges common sense and affective knowledge and visualize it in a multi-dimensional vector space, which we call SenticSpace. In particular we blend ConceptNet and WordNet-Affect and use dimensionality reduction on the resulting knowledge base to build a 24-dimensional vector space in which different vectors represent different ways of making binary distinctions among concepts and sentiments.